Comparison details. In this part, we fully compare our proposed MM-RealSR with recent modulation (i.e., CUGAN [1]}) and non-modulation methods. The non-modulation methods include several well-known SR methods (e.g., RealSR [2]} and ESRGAN [3]}) and some recent top-performing methods (e.g., BSRGAN [4]} and Real-ESRGAN [5]}). Real-ESRGAN is also the theoretical upper bound of our proposed MM-RealSR, as MM-RealSR needs to learn another objective for interactive modulation. Note that CUGAN must be provided with the degradation levels (\(S_b,\ S_n \in [0,\ 1]\) ), which are not available in real-world test sets, i.e., RealSRSet and AIM19. Thus, we divide the noise and blur degradation levels into 11 points uniformly distributed between 0 and 1, and then traversed all cases (121 cases in total). We select the best result (lowest LPIPS) for each image. Unlike the above two settings, our MM-RealSR can be used either with the degradation scores estimated by our UDEM module or with the user input scores. In this part, to make a fair comparison, the input degradation score of our MM-RealSR is provided by UDEM without manual adjustment.
